Table 2 Performance of rrBLUP (average r 2) on the traits of four real data sets under the traditional encoding vs. the hybrid encodings vs. the target-based encoding. We also show the improvements of the hybrid encodings over the traditional encoding
From: Does encoding matter? A novel view on the quantitative genetic trait prediction problem
Data set | Traditional encoding | Hybrid one (improvement) | Hybrid two (improvement) | Target-based |
|---|---|---|---|---|
Rice: Pericarp.color | 0.433 | 0.499 (16 %) | 0.504 (16.4 %) | 0.493 |
Rice: Protein.content | 0.176 | 0.176 (1 %) | 0.177 (1 %) | 0.177 |
Pig: Trait 2 | 0.237 | 0.238 (1 %) | 0.239 (1 %) | 0.236 |
Pig: Trait 4 | 0.203 | 0.218 (7 %) | 0.218 (7 %) | 0.207 |
QTLMAS: Trait 1 | 0.358 | 0.36 (1 %) | 0.361 (1 %) | 0.36 |
QTLMAS: Trait 2 | 0.187 | 0.179 (-4 %) | 0.18 (-4 %) | 0.178 |
Maize: Flint 1 TASS | 0.47 | 0.492 (5 %) | 0.492 (5 %) | 0.475 |
Maize: Flint 2 DMC | 0.301 | 0.311 (2.5 %) | 0.308 (2.3 %) | 0.289 |
Maize: Flint 3 DM_Yield | 0.057 | 0.07 (20 %) | 0.068 (19 %) | 0.062 |
Maize: Dent 1 Tass | 0.59 | 0.615 (4.4 %) | 0.616 (4.4 %) | 0.593 |
Maize: Dent 2 DMC | 0.562 | 0.58 (3.2 %) | 0.58 (3.2 %) | 0.582 |
Maize: Dent 3 DM_Yield | 0.321 | 0.343 (8.6 %) | 0.349 (8.7 %) | 0.346 |